Nothing new under the sun: Mediaeval line plot, circa 1010. Image: wikimedia

In the session we watched Hans Rosling’s “200 countries and 200 years in 4 minutes”, which we (hopefully) agreed is something to aspire to. Combined with his enthusiastic presentation, the visualisations in this clip support a clear narrative and help us understand this complex dataset.

The plot he builds plot is interesting because it uses many different visual attributes (aesthetics) to express features in the data:

  • X and Y axes
  • Size of the points
  • Colour
  • Time (in the animation)

These features are carefully selected to highlight important features of the data and support the narrative he provides. Although we need to have integrity in our plotting (we saw bad examples in the session), this narrative aspect of a plot is important: we always need to consider our audience.

Multi-dimensional plotting

This sounds fancy, but as we saw it just means linking different visual or perceptual features of a plot to different variables in the data.

And Rosling’s plot is appealing/informative because it adds multiple dimensions of colour and size, and uses a special logarithmic scale for the x-axis.

Defining dimensions/aesthetics in ggplot

As a reminder: ggplot uses the term aesthetics to refer to different dimensions of a plot. ‘Aesthetics’ refers to ‘what things look like’, and the aes() command in ggplot creates links between variables (columns in the dataset) to visual features of the plot. This is called a mapping.

There are x visual features (aesthetics) of plots we will use in this session:

  • x and y axes
  • colour
  • size (of a point, or thickness of a line)
  • shape (of points)
  • linetype (i.e. dotted/patterned or solid)

Recreate the Rosling plot

Rosling’s plot looked something like this:

To create a (slightly simplified) version of the plot above, the code would look something like this:

development %>%
  filter(BLANK==BLANK)  %>%
  ggplot(aes(x=BLANK,
             y=BLANK,
             size=BLANK,
             color=BLANK)) +
  geom_point()

I have removed some parts of the code. Your job is to edit the parts which say <BLANK> and replace them with the names of variables from the development dataset (available in the psydata package).

Some hints:

  • Check the title of the figure above to work out which rows of the data you need to plot (and so define the filter)
  • All the BLANKs represent variable names in the dataset. You can see a list of the column names available by typing glimpse(development)
  • Use mutate to alter the population column to represent millions
  • If you are confused by the filter(BLANK==BLANK) check the title of the plot above. Remember that filter selects particular rows from the data, so we can use it to restrict what is shown. What data do we need to select for this plot?

Summary so far

  • Plots can have multiple dimensions; that is, they can display several variables at once
  • Colour, shape, size and line-type are common ways of displaying Dimensions
  • In ggplot, these visual features are called aesthetics
  • The mapping between visual features and variables is created using the aes() command

Using multiple layers

When visualising data, there’s always more than one way to do things. As well as plotting different dimensions, different types of plot can highlight different features of the data. In ggplot, different types of plots are called geometries. Multiple layers can be combined in the same plot by adding together commands which have the prefix geom_.

As we have already seen, we can use geom_point(...) to create a scatter plot:

Life expectancy and GDP in Asia

Life expectancy and GDP in Asia

To add additional layers to this plot, we can add extra geom_<NAME> functions. For example, geom_smooth overlays a smooth line to any x/y plot:

development %>%
  filter(continent=="Asia") %>%
  ggplot(aes(life_expectancy, gdp_per_capita)) +
  geom_point() +
  geom_smooth()

Explanation of the command: We added + geom_smooth() to our previous plot. This means we now have two geometries added to the same plot: geom_point and geom_smooth.

Explanation of the output: If you run the command above you will see some warning messages which say geom_smooth() using method = 'gam' and formula 'y ~ s(x, bs = "cs")'. You can ignore this for the moment. The plot shown is the same as the scatterplot before, but now has a smooth blue line overlaid. This represents the local-average of GDP, for each level of lifeExp. There is also a grey-shaded area, which represents the standard error of the local average (again there will be more on this later).

Make a smoothed-line plot

  1. Use the incomes dataset from psydata. Create a scatter plot of any two continuous variables.

  2. Add a smoothed line to the plot using geom_smooth()

  3. Add the colour or size aesthetics to the plot above (i.e., using a third column of data)

Making a straight-line plot

geom_smooth() can use a variety of different methods to calculate where to draw the line through the points.

One useful feature is that we can use a linear model to draw a straight line through the points. These straight lines are the same as those calculated by a regression model, which we cover in more detail later in the course.

Adding a straight line is a helpful way to check if the assumption of linearity made by correlations (and regression) is valid.

For example, if we add a line to a plot of power and mpg in the fuel dataset, we can see that a straight line doesn’t fit the data all that well:

fuel %>% 
  ggplot(aes(power, mpg)) + 
  geom_point() + 
  geom_smooth(method=lm, se=F)

Explanation of the code: We used geom_smooth() as before, but added method=lm to force R to draw a straight line. We also added se=F to remove the shaded area representing the standard error of the line.

Explanation of the output: The plot features a straight line, rather than a smooth curve. We can see more clearly that the relationship between power and mpg is NOT linear (so using a correlation would not be appropriate).

  1. Draw a plot using the funimagery data showing the relationship between kg1 and kg2. Add a straight line to this plot.

  2. Do these variables exhibit a linear relationship?

We can see that a straight line describes the relationship quite well. All points are clustered on the line.

Facets

As we add layers our plots become more complex. We run into trade-offs between information density and clarity.

To give one example, this plot shows life expectancies for each country in the gapminder data, plotted by year:

development %>%
  ggplot(aes(year, life_expectancy, group=country)) +
  geom_smooth(se=FALSE)

Explanation: This is another x/y plot. However this time we have not added points, but rather smoothed lines (one for each country).

Explanation of the code:We have created an x/y plot as before, but this time we only added geom_smooth (and not geom_point), so we can’t see the individual datapoints. We have also added the text group=country which means we see one line per-country in the dataset. Finally, we also added se=FALSE which hides the shaded area that geom_smooth adds by default representing the standard error of the line.

Comment on the result: It’s pretty hard to read!

To increase the information density, and explore patterns within the data, we might add another dimension and aesthetic. The next plot colours the lines by continent:

development %>%
  ggplot(aes(year, life_expectancy, colour=continent, group=country)) +
  geom_smooth(se=FALSE)

However, even with colours added it’s still a bit of a mess. We can’t see the differences between continents easily. To clean things up we can use a technique called facetting:

development %>%
  ggplot(aes(year, life_expectancy, group=country)) +
  geom_smooth(se=FALSE) +
  facet_grid(~continent)

Explanation: We added the text + facet_grid(.~continent) to our earlier plot, but removed the part that said color=continent. This made ggplot create individual panels for each continent. Splitting the graph this way makes it somewhat easier to compare the differences between continents.

Use facetting

Use the iris dataset which is built into R.

  1. Try to recreate this plot, by adapting the code from the example above:

  1. Create a new plot which uses colours to distinguish species and does not use Facets

  2. In this example, which plot do you prefer? What influences when facets are more useful the just using colour?

There’s no right answer here, but for this example I prefer the coloured plot to the faceted one. The reason is that there are only 3 species in this dataset, and the points for each don’t overlap much. This means it is easy to distinguish them, even in the combined plot. But, if there were many different species it might be helpful to use facets instead.

Our decisions should be driven by what we are trying to communicate with the plot. What was the research question that motivated us to draw it?

  1. Try replacing facet_grid(~continent) with facet_grid(continent~.). What happens?

  2. With the development example from above, try replacing facet_grid with facet_wrap(~continent). What happens?

  3. To see more facetting examples, see the ggplot cookbook documentation.

‘Real world’ tasks

These tasks are optional extensions; only complete them if you have time. We will be doing more of this practice in the next session.

  1. Use the fuel data. Make two plots which show the relationship between engine size and power. In the first, use colour to distinguish cars with different numbers of gears. In the second use facets to make the same distinction. Which do you think is more helpful and why?

  2. Is there a relationship between age and weight-lost in the funimagery data? Make a plot to explore this. Is the pattern consistent between genders and intervention groups? Which plots are most helpful for this?